SoroushVahidi/scidocs huggingface dataset:
The Pervasive Effects of an Antibiotic on the Human Gut Microbiota, as Revealed by Deep 16S rRNA Sequencing
(2008)

The human intestinal microbiota is essential to the health of the host and plays a role in nutrition, development, metabolism, pathogen resistance, and regulation of immune responses. Antibiotics may disrupt these coevolved interactions, leading to acute or chronic disease in some individuals. Our understanding of antibiotic-associated disturbance of the microbiota has been limited by the poor sensitivity, inadequate resolution, and significant cost of current research methods. The use of pyrosequencing technology to generate large numbers of 16S rDNA sequence tags circumvents these limitations and has been shown to reveal previously unexplored aspects of the "rare biosphere." We investigated the distal gut bacterial communities of three healthy humans before and after treatment with ciprofloxacin, obtaining more than 7,000 full-length rRNA sequences and over 900,000 pyrosequencing reads from two hypervariable regions of the rRNA gene. A companion paper in PLoS Genetics (see Huse et al., doi: 10.1371/journal.pgen.1000255) shows that the taxonomic information obtained with these methods is concordant. Pyrosequencing of the V6 and V3 variable regions identified 3,300-5,700 taxa that collectively accounted for over 99% of the variable region sequence tags that could be obtained from these samples. Ciprofloxacin treatment influenced the abundance of about a third of the bacterial taxa in the gut, decreasing the taxonomic richness, diversity, and evenness of the community. However, the magnitude of this effect varied among individuals, and some taxa showed interindividual variation in the response to ciprofloxacin. While differences of community composition between individuals were the largest source of variability between samples, we found that two unrelated individuals shared a surprising degree of community similarity. In all three individuals, the taxonomic composition of the community closely resembled its pretreatment state by 4 weeks after the end of treatment, but several taxa failed to recover within 6 months. These pervasive effects of ciprofloxacin on community composition contrast with the reports by participants of normal intestinal function and with prior assumptions of only modest effects of ciprofloxacin on the intestinal microbiota. These observations support the hypothesis of functional redundancy in the human gut microbiota. The rapid return to the pretreatment community composition is indicative of factors promoting community resilience, the nature of which deserves future investigation.
An FPGA-based processor for training convolutional neural networks
(2017)

Convolutional neural networks (CNNs) have gained great success in various computer vision applications. However, training a CNN model is computation-intensive and time-consuming. Hence training is mainly processed on large clusters of high-performance processors like server CPUs and GPUs. In this paper, we propose an FPGA-based processor design to accelerate the training process of CNNs. We first analyze the operations in all types of CNN layers in the training process. A uniform computation engine design is proposed to efficiently carry out all kinds of operations based on the analysis. Then a scalable accelerator framework is presented that exploits the parallelism further by unrolling the loops in two levels. The proposed accelerator design is demonstrated by implementing a processor on the Xilinx ZU19EG FPGA working at 200 MHz. The evaluation results on a group of CNN models show that our processor is 5.7 to 10.7-fold faster than the software implementations on the Intel Core i5-4440 CPU(@3.10GHz).
Transformer Health Condition Monitoring Through GSM Technology
(2012)

. Transformers are a vital part of the transmission and distribution system. Monitoring transformers for problems before they occur can prevent faults that are costly to repair and result in a loss of service. Current systems can provide information about the state of a transformer, but are either offline or very expensive to implement. Transformers being the essential part of power transmission system are expensive, as is the cost of power interruptions. Because of the cost of scheduled and unscheduled maintenance, especially at remote sites, the utility industry has begun investing in instrumentation and monitoring of transformer. On-line transformer diagnostics using conventional technologies like carrier power line communication, Radio frequency based control system, and Supervisory control and data acquiring systems, Distributed control systems and Internet based communications are having their own limitations. GSM is an open, digital cellular technology used for transmitting mobile voice and data services. This project objective is to develop low cost solution for monitoring health condition of remotely located distribution transformers using GSM technology to preent premature failure of distribution transformers and improving reliability of services to the customers. An Embedded based hardware design is developed to acquire data from electrical sensing system. It consists of a sensing system, signal conditioning electronic circuits, advanced embedded hardware for middle level computing, a powerful computer network for further transmission of data to various places. A powerful GSM networking is designed to send data from a network to other network for proper corrective action at the earliest. Any change in parameters of transmission is sensed to protect the entire transmission and distribution. The performance of prototype model developed is tested at laboratory for monitoring various parameters like transformer over load, voltage fluctuations, over temperature, oil quality and level etc.
Learning Joint Multilingual Sentence Representations with Neural Machine Translation
(2017)

In this paper, we use the framework of neural machine translation to learn joint sentence representations across six very different languages. Our aim is that a representation which is independent of the language, is likely to capture the underlying semantics. We define a new crosslingual similarity measure, compare up to 1.4M sentence representations and study the characteristics of close sentences. We provide experimental evidence that sentences that are close in embedding space are indeed semantically highly related, but often have quite different structure and syntax. These relations also hold when comparing sentences in different languages.
Periodic Leaky-Wave Antenna for Millimeter Wave Applications Based on Substrate Integrated Waveguide
(2010)

Substrate integrated waveguides (SIW) are built up of periodically arranged metallic via-holes or via-slots. The leakage loss of SIW structures increases with the distance between the via-holes or via-slots. An open periodic waveguide with a large via distance supports the propagation of leaky-wave modes and can thus be used for the design of a leaky-wave antenna. In this paper, this leakage loss is studied in detail and used to design a periodic leaky-wave antenna. The proposed concept represents an excellent choice for applications in the millimeter-wave band. Due to its versatility, the finite difference frequency domain method for periodic guided-wave or leaky-wave structures is used to analyze the characteristics of the proposed periodic leaky-wave antenna. Two modes (TE10 and TE20) are investigated and their different leaky-wave properties are analyzed. Based on the proposed leaky-mode analysis method, a novel periodic leaky-wave antenna at 28-34 GHz is designed and fabricated.
Open Information Extraction on Scientific Text: An Evaluation
(2018)

Open Information Extraction (OIE) is the task of the unsupervised creation of structured information from text. OIE is often used as a starting point for a number of downstream tasks including knowledge base construction, relation extraction, and question answering. While OIE methods are targeted at being domain independent, they have been evaluated primarily on newspaper, encyclopedic or general web text. In this article, we evaluate the performance of OIE on scientific texts originating from 10 different disciplines. To do so, we use two state-of-the-art OIE systems using a crowd-sourcing approach. We find that OIE systems perform significantly worse on scientific text than encyclopedic text. We also provide an error analysis and suggest areas of work to reduce errors. Our corpus of sentences and judgments are made available.
Complications of botulinum toxin A use in facial rejuvenation.
(2005)

The esthetic application of botulinum toxin type A (Botox) is a safe treatment modality; nevertheless complications can occur as a result of patient-and physician-related factors. Fortunately, adverse effects and undesirable sequelae after Botox injections are temporary. Complications may be more serious in patients who have more severe rhytids (which require more Botox), previous facial plastic surgery (altered anatomy), and those who have pre-existing neuromuscular disease. The physician can reduce complications by using proper injection techniques, appropriate regional Botox dosing, and by being conservative in the overall approach to Botox-mediated facial rejuvenation.
Adipose Hypertrophy Following Cryolipolysis.
(2015)

Cryolipolysis has become a popular non-invasive treatment for unwanted fatty collections. The benefits are minimal down time, short recovery time, and decreased pain, although pain is the most frequent complaint. The author recently treated a patient who presented with an enlarging adipose collection on his lower abdomen following cryolipolysis with CoolSculpting(®) (ZELTIQ Aesthetics, Inc., Pleasanton, CA).
Real time personalized search on social networks
(2015)

Internet users are shifting from searching on traditional media to social network platforms (SNPs) to retrieve up-to-date and valuable information. SNPs have two unique characteristics: frequent content update and small world phenomenon. However, existing works are not able to support these two features simultaneously. To address this problem, we develop a general framework to enable real time personalized top-k query. Our framework is based on a general ranking function that incorporates time freshness, social relevance and textual similarity. To ensure efficient update and query processing, there are two key challenges. The first is to design an index structure that is update-friendly while supporting instant query processing. The second is to efficiently compute the social relevance in a complex graph. To address these challenges, we first design a novel 3D cube inverted index to support efficient pruning on the three dimensions simultaneously. Then we devise a cube based threshold algorithm to retrieve the top-k results, and propose several pruning techniques to optimize the social distance computation, whose cost dominates the query processing. Furthermore, we optimize the 3D index via a hierarchical partition method to enhance our pruning on the social dimension. Extensive experimental results on two real world large datasets demonstrate the efficiency and the robustness of our proposed solution.
D.L.D., Dynamic Lighting Design: Parametric Interactive Lighting Software in Urban Public Space
(2013)

D.L.D., Dynamic Lighting Design, is a three year, E.U. funded, ongoing research project, concerning ways of designing dynamic luminous environments for pedestrians in open public spaces. This paper presents a set of ideas about how space and the surface of architecture are defined in a dynamic luminous environment, which enhance pedestrian lighting and encourage people to walk and visit public spaces. The D.L.D., parametric interactive lighting software in urban public space, will also be outlined in this paper.
Motivated Reinforcement Learning
(2001)

The standard reinforcement learning view of the involvement of neuromodulatory systems in instrumental conditioning includes a rather straightforward conception of motivation as prediction of sum future reward. Competition between actions is based on the motivating characteristics of their consequent states in this sense. Substantial, careful, experiments reviewed in Dickinson & Balleine, 12,13 into the neurobiology and psychology of motivation shows that this view is incomplete. In many cases, animals are faced with the choice not between many different actions at a given state, but rather whether a single response is worth executing at all. Evidence suggests that the motivational process underlying this choice has different psychological and neural properties from that underlying action choice. We describe and model these motivational systems, and consider the way they interact.
CleanM: An Optimizable Query Language for Unified Scale-Out Data Cleaning
(2017)

Data cleaning has become an indispensable part of data analysis due to the increasing amount of dirty data. Data scientists spend most of their time preparing dirty data before it can be used for data analysis. At the same time, the existing tools that attempt to automate the data cleaning procedure typically focus on a specific use case and operation. Still, even such specialized tools exhibit long running times or fail to process large datasets. Therefore, from a user’s perspective, one is forced to use a different, potentially inefficient tool for each category of errors. This paper addresses the coverage and efficiency problems of data cleaning. It introduces CleanM (pronounced clean’em), a language which can express multiple types of cleaning operations. CleanM goes through a three-level translation process for optimization purposes; a different family of optimizations is applied in each abstraction level. Thus, CleanM can express complex data cleaning tasks, optimize them in a unified way, and deploy them in a scaleout fashion. We validate the applicability of CleanM by using it on top of CleanDB, a newly designed and implemented framework which can query heterogeneous data. When compared to existing data cleaning solutions, CleanDB a) covers more data corruption cases, b) scales better, and can handle cases for which its competitors are unable to terminate, and c) uses a single interface for querying and for data cleaning.
A Teachable-Agent Arithmetic Game's Effects on Mathematics Understanding, Attitude and Self-efficacy
(2011)

A teachable-agent arithmetic game is presented and evaluated in terms of student performance, attitude and self-eff icacy. An experimental prepost study design was used, enrolling 153 3 rd and 5 grade students in Sweden. The playing group showed significantly larger gains i math performance and self-efficacy beliefs, but not in general attitude towards math, compared to control groups. The contributions in relation to pr evious work include a novel educational game being evaluated, and an emphasis o n elf-efficacy in the study as a strong predictor of math achievements.
Video-based face recognition using ensemble of haar-like deep convolutional neural networks
(2017)

Growing number of surveillance and biometric applications seek to recognize the face of individuals appearing in the viewpoint of video cameras. Systems for video-based FR can be subjected to challenging operational environments, where the appearance of faces captured with video cameras varies significantly due to changes in pose, illumination, scale, blur, expression, occlusion, etc. In particular, with still-to-video FR, a limited number of high-quality facial images are typically captured for enrollment of an individual to the system, whereas an abundance facial trajectories can be captured using video cameras during operations, under different viewpoints and uncontrolled conditions. This paper presents a deep learning architecture that can learn a robust facial representation for each target individual during enrollment, and then accurately compare the facial regions of interest (ROIs) extracted from a still reference image (of the target individual) with ROIs extracted from live or archived videos. An ensemble of deep convolutional neural networks (DCNNs) named HaarNet is proposed, where a trunk network first extracts features from the global appearance of the facial ROIs (holistic representation). Then, three branch networks effectively embed asymmetrical and complex facial features (local representations) based on Haar-like features. In order to increase the discriminativness of face representations, a novel regularized triplet-loss function is proposed that reduces the intra-class variations, while increasing the inter-class variations. Given the single reference still per target individual, the robustness of the proposed DCNN is further improved by fine-tuning the HaarNet with synthetically-generated facial still ROIs that emulate capture conditions found in operational environments. The proposed system is evaluated on stills and videos from the challenging COX Face and Chokepoint datasets according to accuracy and complexity. Experimental results indicate that the proposed method can significantly improve performance with respect to state-of-the-art systems for video-based FR.
Double Refinement Network for Efficient Indoor Monocular Depth Estimation
(2018)

Monocular Depth Estimation is an important problem of Computer Vision that may be solved with Neural Networks and Deep Learning nowadays. Though recent works in this area have shown significant improvement in accuracy, state-of-the-art methods require large memory and time resources. The main purpose of this paper is to improve performance of the latest solutions with no decrease in accuracy. To achieve this, we propose a Double Refinement Network architecture. We evaluate the results using the standard benchmark RGB-D dataset NYU Depth v2. The results are equal to the current state-of-the-art, while frames per second rate of our approach is significantly higher (up to 15 times speedup per image with batch size 1), RAM per image is significantly lower.
The Possibility of an Epidemic Meme Analogy for Web Community Population Analysis
(2007)

The aim of this paper is to discuss the possibility of understanding human social interaction in web communities by analogy with a disease propagation model from epidemiology. When an article is submitted by an individual to a social web service, it is potentially influenced by other participants. The submission sometimes starts a long and argumentative chain of articles, but often does not. This complex behavior makes management of server resources difficult and a more theoretical methodology is required. This paper tries to express these complex human dynamics by analogy with infection by a virus. In this first report, by fitting an epidemiological model to Bulletin Board System (BBS) logs in terms of a numerical triple, we show that the analogy is reasonable and beneficial because the analogy can estimate the community size despite the submitter’s information alone being observable.
Millimeter-wave Gbps Broadband Evolution towards 5G: Fixed Access and Backhaul
(2015)

— As wireless communication evolves towards 5G, both fixed broadband and mobile broadband will play a crucial part in providing the Gbps infrastructure for a connected society. This paper proposes a Millimeter-wave Gbps Broadband (MGB) system as the solution to two critical problems in this evolution: last-mile access for fixed broadband and small cell backhaul for mobile broadband. The key idea is to use spectrum that is already available in the millimeter wave bands for fixed wireless access with optimized dynamic beamforming and massive MIMO infrastructure to achieve high capacity with wide area coverage. This paper explains the MGB concept and describes potential array architectures for realizing the system. Simulations demonstrate that with 500 MHz of bandwidth (at 39 GHz band) and 28 dBm transmission power (55 dBm EIRP), it is possible to provide more than 11 Gbps backhaul capacity for 96 small cells within 1-km radius.
Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment
(2010)

Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a content analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research.
Information network or social network?: the structure of the twitter follow graph
(2014)

In this paper, we provide a characterization of the topological features of the Twitter follow graph, analyzing properties such as degree distributions, connected components, shortest path lengths, clustering coefficients, and degree assortativity. For each of these properties, we compare and contrast with available data from other social networks. These analyses provide a set of authoritative statistics that the community can reference. In addition, we use these data to investigate an often-posed question: Is Twitter a social network or an information network? The "follow" relationship in Twitter is primarily about information consumption, yet many follows are built on social ties. Not surprisingly, we find that the Twitter follow graph exhibits structural characteristics of both an information network and a social network. Going beyond descriptive characterizations, we hypothesize that from an individual user's perspective, Twitter starts off more like an information network, but evolves to behave more like a social network. We provide preliminary evidence that may serve as a formal model of how a hybrid network like Twitter evolves.
Rule Based Machine Translation Combined with Statistical Post Editor for Japanese to English Patent Translation
(2007)

Since sentences in patent texts are long, they are difficult to translate by a machine. Although statistical machine translation is one of the major streams of the field, long patent sentences are difficult to translate not using syntactic analysis. We propose the combination of a rule based method and a statistical method. It is a rule based machine translation (RMT) with a statistical based post editor (SPE). The evaluation by the NIST score shows RMT+SPE is more accurate than RMT only. Manual checks, however, show the outputs of RMT+SPE often have strange expressions in the target language. So we propose a new evaluation measure NMG (normalized mean grams). Although NMG is based on n-gram, it counts the number of words in the longest word sequence matches between the test sentence and the target language reference corpus. We use two reference corpora. One is the reference translation only the other is a large scaled target language corpus. In the former case, RMT+SPE wins in the later case, RMT wins.
Designing web usability
(2000)

Where you can find the designing web usability easily? Is it in the book store? On-line book store? are you sure? Keep in mind that you will find the book in this site. This book is very referred for you because it gives not only the experience but also lesson. The lessons are very valuable to serve for you, that's not about who are reading this designing web usability book. It is about this book that will give wellness for all people from many societies.